tensorflow学习笔记14

训练神经网络4

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import input_data

mnist = input_data.read_data_sets('data/',one_hot=True) #one_hot=True编码格式为01编码
n_hidden_1 = 256
n_hidden_2 = 128
n_input = 784
n_classes = 10

x = tf.placeholder("float",[None,n_input])
y = tf.placeholder("float",[None,n_classes])

stddev = 0.1
weights = {
    'w1':tf.Variable(tf.random.normal([n_input,n_hidden_1],stddev=stddev)),
    'w2':tf.Variable(tf.random.normal([n_hidden_1,n_hidden_2],stddev=stddev)),
    'out':tf.Variable(tf.random.normal([n_hidden_2,n_classes],stddev=stddev))
}
biases = {
    'b1':tf.Variable(tf.random.normal([n_hidden_1])),
    'b2':tf.Variable(tf.random.normal([n_hidden_2])),
    'out':tf.Variable(tf.random.normal([n_classes]))
}
print("NETWORK READY")

def multilayer_perceptron(_X,_weights,_biases):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X,_weights['w1']),_biases['b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1,_weights['w2']),_biases['b2']))
    return (tf.matmul(layer_2,_weights['out']) + _biases['out'])

pred = multilayer_perceptron(x, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(pred,y)) #tensorflow中已有的交叉熵函数
optm = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(cost)
corr = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accr = tf.reduce_mean(tf.cast(corr,"float"))

init = tf.compat.v1.global_variables_initializer()
print("FUNCTIONS READY")

training_epochs = 20 #一共迭代20次
batch_size = 100 #每一次迭代选择100个样本
display_step = 4

sess = tf.compat.v1.Session()
sess.run(init)
for epoch in range(training_epochs):
    avg_cost = 0
    num_batch = int(mnist.train.num_examples/batch_size)
    for i in range(num_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        sess.run(optm,feed_dict={x:batch_xs,y:batch_ys})
        feeds = {x:batch_xs,y:batch_ys}
        avg_cost += sess.run(cost,feed_dict=feeds)/num_batch
    if epoch % display_step == 0: #每四轮打印一次
        print("Epoch: %02d/%02d cost: %.6f" % (epoch, training_epochs, avg_cost))
        feeds = {x: batch_xs, y: batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)  # 训练集的精度
        print("TRAIN ACCURACY: %.3f" % (train_acc))
        feeds = {x: mnist.test.images, y: mnist.test.labels}
        test_acc = sess.run(accr, feed_dict=feeds)  # 测试集的精度
        print("TEST ACCURACY: %.3f" % (test_acc))

print("FINISHED")